Choosing the Right Research Input for the Decision at Hand
Experienced PMMs rely on the full range of research tools in their toolbox and use them intentionally. They know that sometimes good enough is good enough, and other times it isn’t. The skill is knowing which is which. In our conversations with our clients, here is what we’ve heard.
An AI-First Starting Point
Most B2B tech and SaaS companies take an AI-first approach to research. Some of this stems from personal choice. Some is pressure from senior management to incorporate AI into workflows.
Generative AI is a great way to get a market overview, especially early in a project. Our clients use deep research tools to gain a high-level view of new markets and competitors. Product marketers who are AI-savvy can take things all the way down to building basic ICPs and buyer profiles. This takes more than simple prompts, but it’s possible with some training and practice.
As helpful as GenAI can be, most B2B marketers recognize the key limitations of AI-driven research: it relies on information already available online (and accessible to competitors as well). In addition, the output does not reflect the POV of your specific prospect set.
The relevance of these limitations depends on how you use the insights. Often, GenAI insights are sufficient to get up to speed on a market quickly or to gather directional context. However, they will typically not be enough to justify strategic decisions and investments.
Insights from Sales and Customer Conversations
Product marketers talk to sales reps, account reps, and customers. They routinely interview sales and account reps to learn what customers are asking for and what they care about. Even better is talking directly to a customer, which is like gold. This can be formal, scheduled conversations or informal chats with clients at customer events and conferences. The challenge of these conversations is their time-consuming nature, the difficulty of scheduling time with customers, and inherent sampling bias.
The most significant limitation, depending on how the insights are used, is that the information typically comes from customers, not prospects. For some decisions, this is fine; for others, it can lead to inaccurate market assumptions.
Analyst Insights
Analyst Firms provide the 30,000-foot view and a language framework that resonates with internal stakeholders. Many product marketers have access to analyst reports (e.g., Gartner, Forrester) and to analysts themselves. Analysts can discuss what they are hearing in the market, where they think the market is heading, and how they expect it to respond to a new product, pricing strategy, or messaging.
They typically cannot provide specific feedback on individual questions, such as whether a specific packaging strategy will be acceptable to the market. Instead, their feedback is couched in what they’ve seen with other vendors and overall trends. To some degree, they resemble the insights GenAI can provide about broad market trends, but with an expert’s interpretation.
Conversation and Revenue Intelligence Tools
Product marketers have always looked for whatever insights they can squeeze from their CRM or sales data. This helps identify ICPs and potential new segments. Some platforms, such as Gong and Chorus, give access to recordings from sales and service calls. Product marketers can mine this data to identify recurring themes in customer and prospect conversations. The speed and scale of these platforms are amazing, but the amount of content can be overwhelming. AI is making it increasingly easy to search these calls, although it is sometimes better at summarizing than providing insights.
Prospect Perspective
One shortcoming many data streams share is that they do not provide the prospect’s voice specific to your solution. Only primary research provides direct feedback from prospects that you can tailor to your specific questions and decisions. This ability can matter when making strategic decisions about pricing, packaging, entering new segments, and messaging.
Prospect research uniquely reveals…
- Why buyers don’t choose you
- How prospects view competitors and alternatives
- The priority prospects place on the problem your solution solves.
- Willingness to switch and pay for solutions like yours
Compared to low-cost (AI) or shared-cost (Analyst, Gong) solutions, primary research is more expensive per project. That is why it is typically used only for high-stakes decisions.
All these approaches are useful, and none are inherently better than another. For product marketers, it comes down to articulating the uncertainty that needs to be reduced and the relative importance of the decision the research is informing.